KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models

Combining Large Language Models and Food Knowledge Graphs for Personalized Food Recommendation and Recipe Generation

Published

May 20, 2025

Authors: F. Mohbat et al.

Published on Arxiv: 2025-05-20

Link: http://arxiv.org/abs/2505.14629v1

Institutions: Rensselaer Polytechnic Institute

Keywords: large language models, knowledge graph, retrieval-augmented generation, recipe recommendation, personalization, LoRA, FoodKG, nutrition analysis, Phi-3, recipe generation, constrained question answering, benchmark dataset, personal preferences, food computing

Recent advances in large language models (LLMs) and greater availability of food data have spurred research in food understanding, recipe generation, and personalized food recommendation systems. While prior work has leveraged LLMs or knowledge graphs (KGs) separately, few have unified food KG integration with LLMs to address personal preferences, dietary restrictions, and nutritional needs within a single framework.

To address these gaps, the authors introduce a novel approach, with the following main contributions:

Regarding its effectiveness, the results demonstrate:

Building on these findings, the conclusions highlight: